80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize, Base64ToImage
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try:
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from paddle_serving_server_gpu.web_service import WebService, Op
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except ImportError:
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from paddle_serving_server.web_service import WebService, Op
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import logging
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import numpy as np
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import base64, cv2
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import os
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import faiss
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import pickle
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class RecogOp(Op):
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def init_op(self):
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self.seq = Sequential([
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Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
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Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
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True)
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])
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#load index; and return top1
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index_dir = "../../recognition_demo_data_v1.1/gallery_product/index"
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assert os.path.exists(os.path.join(
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index_dir, "vector.index")), "vector.index not found ..."
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assert os.path.exists(os.path.join(
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index_dir, "id_map.pkl")), "id_map.pkl not found ... "
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self.Searcher = faiss.read_index(
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os.path.join(index_dir, "vector.index"))
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with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
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self.id_map = pickle.load(fd)
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def preprocess(self, input_dicts, data_id, log_id):
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(_, input_dict), = input_dicts.items()
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batch_size = len(input_dict.keys())
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imgs = []
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for key in input_dict.keys():
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data = base64.b64decode(input_dict[key].encode('utf8'))
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data = np.fromstring(data, np.uint8)
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im = cv2.imdecode(data, cv2.IMREAD_COLOR)
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img = self.seq(im)
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imgs.append(img[np.newaxis, :].copy())
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input_imgs = np.concatenate(imgs, axis=0)
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return {"x": input_imgs}, False, None, ""
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def postprocess(self, input_dicts, fetch_dict, log_id):
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score_list = fetch_dict["features"]
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return_top_k = 1
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scores, docs = self.Searcher.search(score_list, return_top_k)
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result = {}
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result["label"] = self.id_map[docs[0][0]].split()[1]
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result["dist"] = str(scores[0][0])
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return result, None, ""
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class ProductRecognitionService(WebService):
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def get_pipeline_response(self, read_op):
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image_op = RecogOp(name="recog", input_ops=[read_op])
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return image_op
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uci_service = ProductRecognitionService(name="recog_service")
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uci_service.prepare_pipeline_config("config.yml")
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uci_service.run_service()
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